AIMC Topic: Skin Neoplasms
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Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.
Journal of the European Academy of Dermatology and Venereology : JEADV
Jan 21, 2020
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...
Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.
IEEE journal of biomedical and health informatics
Dec 23, 2019
Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligen...
A Machine-Learning Approach to Identify a Prognostic Cytokine Signature That Is Associated With Nivolumab Clearance in Patients With Advanced Melanoma.
Clinical pharmacology and therapeutics
Dec 19, 2019
Lower clearance of immune checkpoint inhibitors is a predictor of improved overall survival (OS) in patients with advanced cancer. We investigated a novel approach using machine learning to identify a baseline composite cytokine signature via clearan...
Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.
Journal of cutaneous pathology
Nov 27, 2019
PURPOSE: Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most difficult areas in dermatopathology. The goal of this study was to identify proteomic differences, which ...
The impact of patient clinical information on automated skin cancer detection.
Computers in biology and medicine
Nov 18, 2019
Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not ...
Skin cancer diagnosis based on optimized convolutional neural network.
Artificial intelligence in medicine
Nov 8, 2019
Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image pro...
Systematic review of machine learning for diagnosis and prognosis in dermatology.
The Journal of dermatological treatment
Oct 31, 2019
Software systems using artificial intelligence for medical purposes have been developed in recent years. The success of deep neural networks (DNN) in 2012 in the image recognition challenge ImageNet LSVRC 2010 fueled expectations of the potential fo...
Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.
European journal of cancer (Oxford, England : 1990)
Oct 23, 2019
Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms.
Journal of the European Academy of Dermatology and Venereology : JEADV
Oct 8, 2019
BACKGROUND: Machine learning algorithms achieve expert-level accuracy in skin lesion classification based on clinical images. However, it is not yet shown whether these algorithms could have high accuracy when embedded in a smartphone app, where imag...
Dense pooling layers in fully convolutional network for skin lesion segmentation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 7, 2019
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmen...
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